Add data_analysis_script/tests/test_core.py

data_analysis_script/tests/test_core.py

@@ -0,0 +1,54 @@
+import pytest
+import math
+import numpy as np
+
+from data_analysis_script import core
+
+
+def example_data():
+    # Representative measurement records
+    return [
+        {"condition_name": "A", "amplitude": 1.0, "timestamp": 0.1},
+        {"condition_name": "A", "amplitude": 2.0, "timestamp": 0.2},
+        {"condition_name": "B", "amplitude": 5.0, "timestamp": 0.3},
+        {"condition_name": "B", "amplitude": 7.0, "timestamp": 0.4},
+        {"condition_name": "B", "amplitude": 20.0, "timestamp": 0.5},  # outlier
+    ]
+
+
+def test_analyze_data_returns_expected_structure():
+    data = example_data()
+    result = core.analyze_data(data)
+    assert isinstance(result, dict)
+    assert set(result.keys()) == {"median_amplitude", "outlier_rate", "statistical_significance"}
+    assert isinstance(result["median_amplitude"], (float, int))
+    assert isinstance(result["outlier_rate"], (float, int))
+    assert isinstance(result["statistical_significance"], (float, int, np.floating))
+
+
+def test_median_and_outlier_logic():
+    data = example_data()
+    result = core.analyze_data(data)
+    median_manual = np.median([d["amplitude"] for d in data])
+    assert math.isclose(result["median_amplitude"], median_manual, rel_tol=1e-6)
+
+    # For outlier rate, expect roughly one outlier (20 vs lower values)
+    total = len(data)
+    outlier_estimate = 1 / total
+    assert abs(result["outlier_rate"] - outlier_estimate) < 0.3
+
+
+def test_statistical_significance_between_conditions():
+    data = example_data()
+    result = core.analyze_data(data)
+    # p-value should be between 0 and 1
+    assert 0.0 <= result["statistical_significance"] <= 1.0
+
+
+def test_handles_empty_or_invalid_input():
+    with pytest.raises(ValueError):
+        core.analyze_data([])
+
+    invalid = [{"condition_name": "A", "amplitude": "x", "timestamp": 0.1}]
+    with pytest.raises(ValueError):
+        core.analyze_data(invalid)